| --- |
| license: other |
| tags: |
| - cosmos |
| - world-model |
| - gr1 |
| - robotics |
| - diffusion-forcing |
| --- |
| |
| # Cosmos3-Nano GR-1 — Diffusion-Forcing + StateFix checkpoints |
|
|
| PyTorch **Distributed Checkpoint (DCP)** checkpoints from joint WM + Action SFT of |
| NVIDIA `Cosmos3-Nano` (Omni-MoT World Foundation Model) on GR-1, using a causal |
| **diffusion-forcing** schedule with a fixed proprio-state prefix ("statefix"). |
|
|
| Each variant is provided at **iteration 20,000** (the latest checkpoint): |
|
|
| | Subfolder | Horizon | chunk_length | latent_t | Training | |
| |-----------|---------|--------------|----------|----------| |
| | `h65/` | 65-frame | 64 | 17 | 8×B200, FSDP, 45,056-token packing | |
| | `h129/` | 129-frame | 128 | 33 | 8×B200, FSDP, 45,056-token packing | |
| | `h65_joint_1000/` | 65-frame | 64 | 17 | 4×B200, FSDP, 45,056-token packing, **joint** diffusion-forcing (WM + action jointly supervised) | |
|
|
| ## Layout |
|
|
| Each subfolder mirrors the framework's checkpoint layout (full DCP — model + optimizer + |
| scheduler + trainer state), so it can be resumed or evaluated directly: |
|
|
| ``` |
| <variant>/ |
| config.yaml, config.pkl, job_env.yaml, launch_info.yaml |
| checkpoints/ |
| latest_checkpoint.txt |
| iter_000020000/ |
| model/ # FSDP-sharded DCP (.distcp shards + .metadata) |
| optim/ # optimizer state (enables training resume) |
| scheduler/ |
| trainer/ |
| ``` |
|
|
| > **Note:** these are *sharded* DCP checkpoints (`.distcp` + `.metadata`), not |
| > consolidated `safetensors`. Load them with `torch.distributed.checkpoint` via the |
| > Cosmos3 framework, or consolidate to HF format with |
| > `cosmos_framework.scripts.export_model`. |
|
|
